Hans Berends, Hans van der Bij, Mathien Weggeman · IGI Global eBooks · 2011
In most organizations, specialized knowledge is dispersed over organization members (Tsoukas, 1996). Organization members have different educational backgrounds and working experiences and develop different perspectives. Yet, the development and production of complex goods and services normally requires the application of multiple disciplines and perspectives. Therefore, the integration of knowledge is an important task for managers and other organization members (Carlile, 2002; De Boer, Van den Bosch, & Volberda, 1999; Galunic & Rodan, 1998; Grant, 1996a, 1996b; Kogut & Zander, 1992; Okhuysen & Eisenhardt, 2002; Ravasi & Verona, 2000).
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SpringerReference · 2012
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Hans Berends, Hans van der Bij, Mathien Weggeman · IGI Global eBooks · 2008
In most organizations, specialized knowledge is dispersed over organization members (Tsoukas, 1996). Organization members have different educational backgrounds and working experiences and develop different perspectives. Yet, the development and production of complex goods and services normally requires the application of multiple disciplines and perspectives. Therefore, the integration of knowledge is an important task for managers and other organization members (Carlile, 2002; De Boer, Van den Bosch, & Volberda, 1999; Galunic & Rodan, 1998; Grant, 1996a, 1996b; Kogut & Zander, 1992; Okhuysen & Eisenhardt, 2002; Ravasi & Verona, 2000).
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Berends, JJ Hans, Bij, JD Hans van der, Weggeman, MCDP Mathieu · TU/e Research Portal (Eindhoven University of Technology) · 2006
In most organizations, specialized knowledge is dispersed over organization members (Tsoukas, 1996). Organization members have different educational backgrounds and working experiences and develop different perspectives. Yet, the development and production of complex goods and services normally requires the application of multiple disciplines and perspectives. Therefore, the integration of knowledge is an important task for managers and other organization members (Carlile, 2002; De Boer, Van den Bosch, & Volberda, 1999; Galunic & Rodan, 1998; Grant, 1996a, 1996b; Kogut & Zander, 1992; Okhuysen & Eisenhardt, 2002; Ravasi & Verona, 2000).
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Hans Berends, Hans van der Bij, Mathieu Weggeman · IGI Global eBooks · 2011
In most organizations, specialized knowledge is dispersed over organization members (Tsoukas, 1996). Organization members have different educational backgrounds and working experiences and develop different perspectives. Yet, the development and production of complex goods and services normally requires the application of multiple disciplines and perspectives. Therefore, the integration of knowledge is an important task for managers and other organization members (Carlile, 2002; De Boer, Van den Bosch, & Volberda, 1999; Galunic & Rodan, 1998; Grant, 1996a, 1996b; Kogut & Zander, 1992; Okhuysen & Eisenhardt, 2002; Ravasi & Verona, 2000).
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Michael Schneider · 2012
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Antonie Jetter, Hans-Horst Schröder, Jeroen Kraaijenbrink et al. · RePEc: Research Papers in Economics
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Pavel B. Brazdil, Luís Torgo · 1990
. In this paper we are concerned with the problem of acquiring knowledge by integration. Our aim is to construct an integrated knowledge base from several separate sources. The need to merge knowledge bases can arise, for example, when knowledge bases are acquired independently from interactions with several domain experts. As opinions of different domain experts may differ, the knowledge bases constructed in this way will normally differ too. A similar problem can also arise whenever separate knowledge bases are generated by learning algorithms. The objective of integration is to construct one system that exploits all the knowledge that is available and has a good performance. The aim of this paper is to discuss the methodology of knowledge integration, describe the implemented system (INTEG.3), and present some concrete results which demonstrate the advantages of this method. 1. Introduction The areas of knowledge acquisition (KA) and machine learning (ML) have until recently exist...
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Linnéa Wahlstedt · 2014
Current research on knowledge integration offers valuable structural analyses of factors that influence knowledge integration, performance outcomes, and knowledge integration mechanisms. Less attention has been paid to how knowledge integration is carried out over time in cross-functional development projects. This thesis is based on a year-long field study of an Information Systems Development Project. The study shows how the knowledge integration process was repeatedly interrupted by different problems that could not be resolved by merely relying on integration mechanisms that were imposed by the top management. Instead, a bottom-up dynamic evolved where the project members and participating project managers managed to reestablish coordination and knowledge integration through the invention of different ‘collective heuristics’. A novel model of Dynamic Knowledge Integration is presented which claims that knowledge integration contains two interplaying processes; one consisting of different knowledge integration mechanisms and activities, and one consisting of the collective heuristics that were invented and employed when unexpected problems emerged. In general, this research argues that knowledge integration can be understood as a dynamic process, of which both knowledge integration mechanisms and collective heuristics constitute core elements.
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Jeff Zhuk · Cambridge University Press eBooks · 2004
This chapter reveals the dependencies between a natural user interface (NUI) and knowledge technologies; discusses architecture, design, and code samples that integrate knowledge technologies into enterprise applications; and gives the keys to a new breed of software— “Softology,” a mix of software and ontological engineering.
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Ophthalmology Severance · 2015
Sociodemographic and health behavioural factors associated with access to and utilisation of eye care
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Jeroen Kraaijenbrink, Fons Wijnhoven · IGI Global eBooks · 2011
As an academic field, knowledge management has concentrated on the creation, storage, retrieval, transfer, and application of knowledge within organizations, while underexposing external knowledge (e.g., Alavi & Leidner, 2001). Although the importance of external knowledge is well recognized (e.g., Cohen & Levinthal, 1990), there remains a need for a better understanding of the organizational processes through which external knowledge is integrated (Grant, 1996; Ranft & Lord, 2002). In particular, we believe that a holistic view on knowledge integration (KI) is both important and lacking. In this article, we address this lacuna in the literature by proposing a process model of KI consisting of three stages¾identification, acquisition, and utilization of external knowledge. Our objective is to propose a model consisting of modular subprocesses that parsimoniously reflect the variety of KI concepts in the literature. This model is useful to scholars and practitioners because it provides a better understanding of the various KI subprocesses by putting them together in a coherent way. Such understanding serves as bedrock for solving KI problems and for designing KI solutions (cf. Markus, Majchrzak, & Gasser, 2002).
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Jeroen Kraaijenbrink, Fons Wijnhoven · IGI Global eBooks · 2011
As an academic field, knowledge management has concentrated on the creation, storage, retrieval, transfer, and application of knowledge within organizations, while underexposing external knowledge (e.g., Alavi & Leidner, 2001). Although the importance of external knowledge is well recognized (e.g., Cohen & Levinthal, 1990), there remains a need for a better understanding of the organizational processes through which external knowledge is integrated (Grant, 1996; Ranft & Lord, 2002). In particular, we believe that a holistic view on knowledge integration (KI) is both important and lacking. In this article, we address this lacuna in the literature by proposing a process model of KI consisting of three stages¾identification, acquisition, and utilization of external knowledge. Our objective is to propose a model consisting of modular subprocesses that parsimoniously reflect the variety of KI concepts in the literature. This model is useful to scholars and practitioners because it provides a better understanding of the various KI subprocesses by putting them together in a coherent way. Such understanding serves as bedrock for solving KI problems and for designing KI solutions (cf. Markus, Majchrzak, & Gasser, 2002). Request access from your librarian to read this chapter's full text.
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Mitsuru Kodama · WORLD SCIENTIFIC eBooks · 2011
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Tamara Raschka (3459884), Meemansa Sood (14609159), Bruce Schultz (14609162) et al. · Figshare · 2023
Modeling biological mechanisms is a key for disease understanding and drug-target identification. However, formulating quantitative models in the field of Alzheimerâs Disease is challenged by a lack of detailed knowledge of relevant biochemical processes. Additionally, fitting differential equation systems usually requires time resolved data and the possibility to perform intervention experiments, which is difficult in neurological disorders. This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. Our approach, called iVAMBN, resulted in a quantitative model that allowed us to simulate a down-expression of the putative drug target CD33, including potential impact on cognitive impairment and brain pathophysiology. Experimental validation demonstrated a high overlap of molecular mechanism predicted to be altered by CD33 perturbation with cell line data. Altogether, our modeling approach may help to select promising drug targets.
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Martinez, Maria Vanina · Digital Repository at the University of Maryland (University of Maryland College Park) · 2011
Large repositories of data are used daily as knowledge bases (KBs) feeding computer systems that support decision making processes, such as in medical or financial applications. Unfortunately, the larger a KB is, the harder it is to ensure its consistency and completeness. The problem of handling KBs of this kind has been studied in the AI and databases communities, but most approaches focus on computing answers locally to the KB, assuming there is some single, epistemically correct solution. It is important to recognize that for some applications, as part of the decision making process, users consider far more knowledge than that which is contained in the knowledge base, and that sometimes inconsistent data may help in directing reasoning; for instance, inconsistency in taxpayer records can serve as evidence of a possible fraud. Thus, the handling of this type of data needs to be context-sensitive, creating a synergy with the user in order to build useful, flexible data management systems. Inconsistent and incomplete information is ubiquitous and presents a substantial problem when trying to reason about the data: how can we derive an adequate model of the world, from the point of view of a given user, from a KB that may be inconsistent or incomplete? In this thesis we argue that in many cases users need to bring their application-specific knowledge to bear in order to inform the data management process. Therefore, we provide different approaches to handle, in a personalized fashion, some of the most common issues that arise in knowledge management. Specifically, we focus on (1) inconsistency management in relational databases, general knowledge bases, and a special kind of knowledge base designed for news reports; (2) management of incomplete information in the form of different types of null values; and (3) answering queries in the presence of uncertain schema matchings. We allow users to define policies to manage both inconsistent and incomplete information in their application in a way that takes both the user's knowledge of his problem, and his attitude to error/risk, into account. Using the frameworks and tools proposed here, users can specify when and how they want to manage/solve the issues that arise due to inconsistency and incompleteness in their data, in the way that best suits their needs.
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Rey, Christophe, Ennaoui, Karima, Mephu Nguifo, Engelbert et al. · Open Archive Toulouse Archive Ouverte (University of Toulouse) · 2020
The IBC (“Intégration de Bases de Connaissance” - Knowledge Bases Integration) project addresses \nthe question of ontology based data integration, in the context of the MMT (Man Machine Teaming) \ninitiative. It aims at combining data residing in different actors (aircraft, drone, sattelite, … ) \nduring an air mission scenario and providing users with a unified view of all available data, in a \ncommunication constrained environment.
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Ивашенко, В. П. · Репозиторий БГУИР (BSUIR Repository) · 2013
Рассматриваются составляющие и применение средств технологии компонентного проектирования баз знаний в виде однородных семантических сети с теоретико-множественной семантикой для решения задач отладки и интеграции баз знаний.
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Christian Berggren, Anna Bergek, Lars Bengtsson et al. · Oxford University Press eBooks · 2011
Technology-based firms continue to compete primarily on innovation, and are continuously required to present new solutions to an exacting market. As technological complexity and specialization intensifies, firms increasingly need to integrate and co-ordinate knowledge by means of project groups, diversified organizations, inter-organizational partnerships, and strategic alliances. Innovation processes have progressively become interdisciplinary, collaborative, inter-organizational, and international, and a firm's ability to synthesize knowledge across disciplines, organizations, and geographical locations has a major influence on its viability and success. This book demonstrates how knowledge integration is crucial in facilitating innovation within modern firms. This book provides original, detailed empirical studies of prerequisites, mechanisms, and outcomes of knowledge integration processes on several organizational levels, from key individuals, projects, and internal organizations, to collaboration between firms. It stresses the need to understand knowledge integration as a multi-level phenomenon, which requires a broad repertoire of organizational and technical means. It further clarifies the need for strong internal capabilities for exploiting external knowledge, reveals how costs of knowledge integration affect outcomes and strategic decisions, and discusses the managerial implications of fostering knowledge integration, providing practical guidance and support for managers of knowledge integration in high technology enterprises.
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Luı́s Torgo, Miroslav Kubat · 1991
In this paper a methodology of knowledge integration is presented together with some experimental results. The goal of this method is the integration into a single theory of the knowledge obtained by different 'learning agents' . We state that this methodology can also be seen as a way to forget useless parts of theories. We briefly describe how this could be done in two different learning scenarios :- single-agent and multi-agent learning environments. Keywords : Concept learning, knowledge integration, forgetting, single-agent learning, multi-agent learning. 1 . INTRODUCTION Generally speaking, the typical task of a concept learning algorithm is as follows. Given a set of concepts and a set of examples which are said to represent these concepts, try to obtain a set of concept recognition rules (a theory). Each example given to the learning algorithm is previously classified as being an example of a specific concept. So the task of the learning algorithm is to obtain a general conc...
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